from collections import OrderedDict

import torch
import torch.nn as nn


__all__ = ["ResNet", "resnet18", "resnet50"]


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(
        in_planes,
        out_planes,
        kernel_size=3,
        stride=stride,
        padding=dilation,
        groups=groups,
        bias=False,
        dilation=dilation,
    )


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(
        self,
        inplanes,
        planes,
        stride=1,
        downsample=None,
        groups=1,
        base_width=64,
        dilation=1,
        norm_layer=None,
    ):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError("BasicBlock only supports groups=1 and base_width=64")
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
    # while original implementation places the stride at the first 1x1 convolution(self.conv1)
    # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
    # This variant is also known as ResNet V1.5 and improves accuracy according to
    # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.

    expansion = 4

    def __init__(
        self,
        inplanes,
        planes,
        stride=1,
        downsample=None,
        groups=1,
        base_width=64,
        dilation=1,
        norm_layer=None,
    ):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.0)) * groups

        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)

        ### START OF HACK ###
        # These below are flipped in numbering for legacy reasons
        # The true solution is to call the last conv `last_conv` here and BasicBlock
        # And update `get_named_scores` to stop looking for `conv2` and look for `last_conv`
        self.conv3 = conv3x3(width, width, stride, groups, dilation)
        self.bn3 = norm_layer(width)

        self.conv2 = conv1x1(width, planes * self.expansion)
        self.bn2 = norm_layer(planes * self.expansion)
        ### END OF HACK ###

        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class ResNet(nn.Module):
    def __init__(
        self,
        block,
        layer_channels,
        channels,
        num_classes=1000,
        zero_init_residual=False,
        groups=1,
        width_per_group=64,
        replace_stride_with_dilation=None,
        norm_layer=None,
    ):
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.zero_init_residual = zero_init_residual

        self.inplanes = channels[0]
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError(
                "replace_stride_with_dilation should be None "
                "or a 3-element tuple, got {}".format(replace_stride_with_dilation)
            )
        self.groups = groups
        self.base_width = width_per_group

        self.conv1 = nn.Conv2d(
            3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False
        )
        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, self.inplanes, layer_channels[0])
        self.layer2 = self._make_layer(
            block,
            channels[1],
            layer_channels[1],
            stride=2,
            dilate=replace_stride_with_dilation[0],
        )
        self.layer3 = self._make_layer(
            block,
            channels[2],
            layer_channels[2],
            stride=2,
            dilate=replace_stride_with_dilation[1],
        )

        if len(layer_channels) == 4:
            self.layer4 = self._make_layer(
                block,
                channels[3],
                layer_channels[3],
                stride=2,
                dilate=replace_stride_with_dilation[2],
            )
            self.fc = nn.Linear(channels[3] * block.expansion, num_classes)
        else:
            # only three layers
            self.fc = nn.Linear(channels[2] * block.expansion, num_classes)

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))

    def _initialize_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                # kaiming_uniform_ in default conv2d
                nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
            elif isinstance(m, nn.Linear):
                # better accuracy and more stable pruning? - 94.7 accuracy
                nn.init.normal_(m.weight, 0, 0.01)
                # better pruning? - 94.4 accuracy
                # nn.init.xavier_normal_(m.weight)
                nn.init.constant_(m.bias, 0)

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if self.zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )
            name = "ds_block"
        else:
            name = "n_block"

        layers = OrderedDict()
        layers[name + "0"] = block(
            self.inplanes,
            planes,
            stride,
            downsample,
            self.groups,
            self.base_width,
            previous_dilation,
            norm_layer,
        )
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers["n_block" + str(i)] = block(
                self.inplanes,
                planes,
                groups=self.groups,
                base_width=self.base_width,
                dilation=self.dilation,
                norm_layer=norm_layer,
            )

        return nn.Sequential(layers)

    def _forward_impl(self, x):
        # See note [TorchScript super()]
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)

        if hasattr(self, "layer4"):
            x = self.layer4(x)

        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)

        return x

    def forward(self, x):
        return self._forward_impl(x)


def _resnet(
    arch, block, layer_channels, channels, patch_for_smaller_input=False, **kwargs
):
    model = ResNet(block, layer_channels, channels, **kwargs)

    if patch_for_smaller_input:
        # normally 64 but some variants e.g. GraSP set it to 32 so let's respect that
        out_channels = model.conv1.out_channels

        model.conv1 = nn.Conv2d(
            3, out_channels, kernel_size=3, stride=1, padding=1, bias=False
        )
        model.maxpool = nn.Identity()

    return model


def resnet18(pretrained=False, progress=True, **kwargs):
    r"""ResNet-18 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    layer_channels = [64, 128, 256, 512]
    return _resnet("resnet18", BasicBlock, [2, 2, 2, 2], layer_channels, **kwargs)


def resnet50(pretrained=False, progress=True, **kwargs):
    layer_channels = [64, 128, 256, 512]
    return _resnet("resnet50", Bottleneck, [3, 4, 6, 3], layer_channels, **kwargs)


